Applied Operations Research and Financial Modelling in Energy: Practical Applications and Implications

Foreword by Otto Jager
Contents
Introduction: Applied Operations Research and Financial Modeling in Energy
	1 Introduction
	2 Operations Research and Financial Modelling in Energy
	3 Issues Covered in This Book
	4 Concluding Remarks
Optimization Methods on Electricity Generation and Transmission Expansion Planning Problem
	1 Introduction
	2 Mathematical Model
		2.1 Objective Function
		2.2 Constraints and Technical Conditions
		2.3 Final Deterministic Model
	3 Uncertainty
		3.1 Uncertain Electricity Demand and Power Generation by Renewable Sources
		3.2 Uncertain Equipment Failure
	4 Solution Method
	5 Summary
	References
Demand-Driven Electricity Supply Options of Electric Vehicles: Modelling, Simulation, and Management Strategy of Public Charging Stations
	1 Introduction
	2 Related Work
		2.1 Worldwide Current Condition
		2.2 Location and Simulation of CSs
	3 Locating Charging Stations
	4 Simulation Modeling
		4.1 Numerical Results
	5 Pricing Policies and Open Problems
	6 Conclusion
	References
A Review on Smart Energy Management Systems in Microgrids Based on Power Generating and Environmental Costs
	1 Introduction
	2 Renewable Energy and Applications
		2.1 Hydroelectric Power
		2.2 Solar Power
		2.3 Wind Power
		2.4 Other Renewables and Contemporary Trends
	3 Microgrids and Smart Energy Management Systems (SEMS)
		3.1 Smart Energy Management Systems
	4 Conclusion
	References
Measuring Efficiency and Productivity Change in the Turkish Electricity Distribution Sector
	1 Introduction
	2 Literature Review
	3 An Overview of the Turkish Electricity Market
	4 Methodology
		4.1 Efficiency Analysis and DEA
		4.2 Malmquist Productivity Index
		4.3 Tobit Analysis
	5 Application
		5.1 Data and Sample
	6 Results
		6.1 DEA Results
		6.2 Tobit Results
		6.3 Malmquist Productivity Index Results
	7 Conclusions
	Appendix 1
	Appendix 2
	Appendix 3
	References
Price and Volatility Forecasting in Electricity with Support Vector Regression and Random Forest
	1 Introduction
	2 Previous Research
		2.1 Price Forecasting with Machine Learning in Electricity Markets
		2.2 Volatility Forecasting in Electricity Markets
	3 Methods
		3.1 Support Vector Regression
		3.2 Random Forest Modeling
	4 Data and Methodology
		4.1 Data Set
		4.2 Price Forecasting Model
		4.3 Volatility Forecasting Model
		4.4 Metrics
	5 Findings
		5.1 Price Forecasting
		5.2 Volatility Forecasting
	6 Conclusion and Policy Implications
	References
Forecasting the Hydro Inflow and Optimization of Virtual Power Plant Pricing
	1 Introduction
	2 Turkish Electricity Market
	3 The Proposed Methodology
	4 Hydro Inflow Forecasting
	5 Virtual Power Plant Pricing
		5.1 Hydro Power Optimization
		5.2 VPP Pricing
		5.3 Sensitivity to Parameters in Hydro Optimization
		5.4 Volume at Risk
	6 Concluding Comments
	References
Comparing the Renewable Energy Technologies via Forecasting Approaches
	1 Introduction
	2 Literature Review
		2.1 Wind Energy Forecasting
		2.2 Hydro Energy Forecasting
	3 Brief Information of the Applied Methods
		3.1 Principal Component Analysis (PCA)
		3.2 Linear Regression and Polynomial Regression
		3.3 Long Short-Term Memory (LSTM)
		3.4 Gated Recurrent Unit (GRU)
	4 Empirical Study
		4.1 Dataset and Data Preprocessing
		4.2 Empirical Methods
	5 Conclusion
	References
Business Cycles and  Energy Real Options Valuation 
	1 Introduction
	2 The Basic Real Option Valuation with Regime Shifts
		2.1 Valuing the Option to Invest
		2.2 Solution of the Constants and Critical Triggers
		2.3 The Single Regime Case
	3 Valuing Regime Switching Real Options Under Arithmetic Brownian Motion
		3.1 Valuing the Option to Invest in Projects with Regime Switching ABM Cash Flows
		3.2 The Single Regime Case
	4 Numerical Analysis
	5 Conclusions
	References
Understanding the Electricity Switching Behavior of Industrial Consumers: An Empirical Study on an Emerging Market
	1 Introduction
	2 An Overview of Turkish Electricity Retail Market Developments
		2.1 Literature and Theoretical Framework
	3 Methodology
		3.1 Data Description and Procedure
		3.2 Measurement of the Study Variables
	4 Findings
		4.1 The Confirmatory Factor Analyses
		4.2 The Preliminary Analyses
		4.3 Binomial Logistic Regression Model
		4.4 Supplementary Analyses
	5 Discussion
	Appendix 1: Descriptive Statistics
	References
Does the Market Value Clean Innovation? Evidence from US Listed Firms
	1 Introduction
	2 Hypothesis and Literature Review
		2.1 Environment-Friendly Innovation Can Improve a Firm's Financial Performance
		2.2 Environment-Friendly Innovation Can Deteriorate a Firm's Financial Performance
	3 Data and Variables
		3.1 Sample of Firms and Firm-Level Patent Data
		3.2 Limitations of Using Patent Measures as a Proxy for Innovation
		3.3 Key Variables of Interest
		3.4 Descriptive Statistics
	4 Methodology
	5 Empirical Analysis
		5.1 Association Between Tobin's Q and Innovation Productivity Variables
		5.2 Do US Firms Have Sufficient Incentive to Produce Environment-Friendly Technologies?
		5.3 Association Between Disaggregated Innovation Productivity Variables and Tobin's Q: Tests for Time-Varying Capital Market Response
	6 Conclusion and Discussion
	References
The Power Grid: From a Technical to a Finance Issue. Who Bears the Financial Risk?
	1 Introduction
	2 Clearing
	3 The Introduction of the Price and Quantity Risk in the Electricity Market
	4 Balancing in the Electricity Market and Counterparty Risks
	5 Empirical Quantification of Risk
	6 Conclusion
	References